The purpose of this study was to examine the impact of AP level on PA and SB among university freshman students in Beijing, China from November 2017 to April 2018 using objectively-measured PA and SB. Our study found a significantly negative relationship between AP and PA and a positive relationship between AP and SB. With a one level increase in AQI and a 10 µg/m³ increase in PM2.5, hourly total minutes of MVPA, walking steps and kcals of energy expenditure were significantly reduced. A 10 µg/m³ increase in PM2.5 was associated with a significantly increase in SB among participants. The impact of AP on individual-level one-hour PA and SB behavior at a specific time was different. To our best knowledge, this is the first study to use objective methods to determine the effect of hourly AP on PA and SB. In addition, this is the first study to estimate the impact of AP on PA and SB at a specific time.
Our findings on the negative relationship between AP and PA are consistent with existing literature [19, 30–33]. In our study, we found that a one-hour AQI increase one level was associated with a decrease by 9 walking steps in one hour. This study additionally found that a 10 µg/m³ increase in PM2.5 was linked with reduction by 2 walking steps in one hour. Two previous U.S. studies linked one unit (< 10 µg/m³) monthly average PM2.5 increase of AP to be associated with decreasing 0.46% leisure time PA using a cross sectional study from the US Behavioral Risk Factor Surveillance System (BRFSS) survey [31, 34]. Evidence from our previous follow-up studies also found a one unit (44.72–56.6 µg/m³) increase in PM2.5 to discourage outdoor PA 110.67 PASE scores among older adults in China  and to reduce 32.45 weekly MVPA among Chinese college students [19, 35]. However, these previous studies were limited by the potential social bias of self-report measures of PA and therefore were have not been able to examine the hourly effects of AP on objective PA. Only two studies with objectively measured data have reported that the association between AP and PA. Consistent with this study, a study of 153 middle-age adult users of an exercise app reported that AQI increase was associated with participates’ reduction in outdoor PA, such as running, biking, and walking . With this current analysis, we can more precisely use accelerometers to estimate the impact of AQI and PM2.5 on MVPA, energy expenditure, and steps rather than use an exercise app associated with PA. Inconsistent with this study, another study showed that PM2.5 increase had no impact on PA in Beijing among 40 Han Chinese participants in the mean age of 31 years using GT3X accelerometers . A possible explanation for this difference could be that the study had a relatively small sample size and could not account for differences among participants. Based on 340 participants’ wGT3X accelerometers data, we can more confidently suggest that an increase in AQI and PM2.5 increase was associated with a reduction of PA in MVPA, energy expenditure, and walking steps.
This study confirmed findings from previous studies regarding the positive correlation between AP and SB [37–39]. This finding suggests that a one-hour 10 µg/m³ PM2.5 increase was associated with an increase in SB by 0.045 minutes in one hour. Consistent with our previous research, an increase in AP concentration in PM2.5 by one unit (81.16 µg/m³) was associated with an increase in total weekly hours of SB by 6.24 hours among a large sample (12,174) of university freshmen in China based on a cohort study survey . To our knowledge, this is one of the first studies to investigate the impact of AP on SB by hourly use objectively measured GT3X accelerometers. Because of conflicting results in this emerging area and somewhat preliminary of this finding, additional investigations are necessary to fully explore the effect of AP on SB among different groups.
The impact of AP on individual-level PA and SB at a specific time was different. This study is the first to examine the association between AP on PA and SB at a specific time. Stronger negative associations of AQI and PM2.5 AP with MVPA, walking steps and energy expenditure in the morning before 8 am, at 4 pm, at 5 pm and at 7 pm were found. Similarly, stronger positive associations of one hour AQI and one hour PM2.5 on SB at 8 am, 9 am, 11 am, and 7 pm were found. In this study, all participants were recruited from freshmen. Typically, a substantial proportion of freshmen do not have class before 8 am in the morning, and/or after 4 pm. Participants in this study could choose PA or SB behavior according to their own preferences of activities during leisure time. Similar time-specific relationships between built environment and PA were found in the previous studies [40, 41]. However, it is interesting that positive associations of AQI and PM2.5 were found with MVPA, walking steps and energy expenditure in the morning at 10 am and 3 pm. Yet, negative associations of AQI and PM2.5 were found with SB. This could be explained by the fact that there are classes, including physical education class, for a substantial proportion of a freshman students’ schedule between 10 am in the morning and 3 pm in the afternoon. Freshmen often engage in PA when traveling to class (e.g. walking or bicycling to or from class) and may perform more exercise in physical education class, regardless of AP. Therefore, 10 am in the morning and 3 pm in the afternoon associations between AP and PA or SB observed in this study are logical. Thus, this study confirms that the impact of AP on PA and SB are different from patterns of in time-specific associations. This finding is closer to the ‘true’ potential effects of AP on PA and SB.
First, the strengths of this study reside in its objectively measured PA-related behavior and precise reporting of data. Most existing studies on the impact of AP on PA and SB have used subjective methods, allowing for uncontrolled confounding bias due to self-report and limited frequency of PA data. Second, this is one of the first studies to measure the impact of AP on PA and SB by one hour using objective methods. Third, this is the first study to examine time-specific results on the relationship between AP and objectively measured PA and SB. However, a few limitations to this study should be noted. First, on the one hand, we didn’t monitor indoor air pollution and may have bias in this study. A subject’s physical activity participation may not vary by air pollution if an individual is active indoors. On the other hand, all freshmen (the subjects of this study) in Tsinghua University live in the same campus, live in the similar dormitories, take similar transportation (e.g., bicycle), and have similar classrooms (6th teaching building in Tsinghua). The subjects sharing similar classrooms, transportation means, and dormitories may partially offset the disruption the influence of indoor air pollution levels. Second, we could not identify the specific types of PA and SB through using accelerometers to assess PA and SB. Third, all participants were recruited by a convenience sampling. Freshman students from one university cannot represent all university students in Beijing, China or nationwide therefore limiting the generalizability of the study’s findings. Future studies are warranted to produce more generalized estimates.